databricks-spark-structured-streaming

To build a Spark Structured Streaming pipeline, you ingest data from Kafka, parse the payload, and write to Delta using a persistent unique checkpoint location. Selecting a processingTime or availableNow trigger ensures reliable, deterministic production behavior.

Handling late data in Spark Structured Streaming requires explicit watermarking to define the threshold for accepting late records. Watermarks enable automatic state cleanup, preventing unbounded state growth while ensuring correct operational safety in production pipelines.

You need explicit watermarks for state cleanup in Spark streaming workloads when performing stateful operations like stream-stream joins. Without watermarking, state data grows indefinitely, so setting a watermark threshold drops old state and maintains operational safety.

Spark Structured Streaming supports MERGE-based upserts into Delta sinks using foreachBatch. This pattern allows you to apply conditional updates and inserts to existing Delta tables, providing reliable idempotent writes for streaming pipeline integrations.

Spark streaming pipelines fail with checkpoint errors when a persistent unique checkpoint location is missing or reused across queries. Ensuring each streaming query has a dedicated, durable checkpoint path prevents state corruption and maintains production-grade reliability.

You can use stream-static joins in Spark Structured Streaming to enrich real-time streaming data against static batch sources. This pattern does not require a watermark for the static side, allowing you to augment streaming Kafka ingestion with dimensional reference data.